Advanced Machine Learning with Python Training Course

This course explores the application of AI—particularly Machine Learning and Deep Learning—within the automotive sector. It equips participants with the knowledge to identify technologies that can be applied across various scenarios in vehicles, ranging from basic automation and image recognition to advanced autonomous decision-making.

A deep dive into the fundamentals of artificial intelligence illustrates how smart technology is transforming digital strategies, automation processes, and decision-making capabilities within enterprise operations. This course covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, as well as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led live training in Argentina (online or on-site) targets biologists who wish to understand the functioning of AlphaFold and utilize its models to guide their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

An Artificial Neural Network is a computational data model employed in the creation of Artificial Intelligence (AI) systems capable of executing 'intelligent' tasks. Neural Networks are frequently utilized in Machine Learning (ML) applications, which serve as one specific implementation of AI. Deep Learning constitutes a specialized subset of Machine Learning.

Applied AI from Scratch in Python provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methodologies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program assists professionals in implementing practical ML models, evaluating the limitations of algorithms, and completing applied projects aimed at solving real-world problems.

This instructor-led live training Argentina (available online or onsite) is designed for researchers and developers who want to use Chainer to build and train neural networks in Python, while keeping the code easy to debug.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Argentina (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led live training in Argentina (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning structures, empowering agents to make decisions through environmental interaction. This technology drives numerous modern AI innovations, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions via reward-based trial and error.

This instructor-led live training, available online or onsite, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques for building intelligent agents capable of autonomous decision-making in complex settings.

Upon completing this training, participants will be equipped to:

• Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Deploy DRL in real-world scenarios including gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Course Format

• Engaging lectures coupled with guided discussions.
• Practical exercises and hands-on implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• For a customized course version (such as switching from TensorFlow to PyTorch), please contact us to make arrangements.

This instructor-led live training in Argentina (online or onsite) is intended for intermediate developers, data scientists, and AI practitioners aiming to utilize TensorFlow Lite for Edge AI applications.

By the conclusion of this training, attendees will be able to:

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Argentina (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Argentina (online or onsite) targets developers or data scientists who wish to utilize Horovod for distributed deep learning training and scale it to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

• Set up the necessary development environment to begin running deep learning trainings.
• Install and configure Horovod to train models using TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to execute on multiple GPUs.

This course provides a conceptual foundation in neural networks, with a broad overview of machine learning algorithms and deep learning techniques, including their algorithms and practical applications.

Part-1 (40%) of the training focuses heavily on fundamentals, helping you select the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part-3 (40%) is extensively based on TensorFlow, the API for Google's open-source library for Deep Learning. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

This course is designed for engineers who wish to utilize TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of Deep Neural Networks (DNN), CNNs, and RNNs
• grasp TensorFlow’s structure and deployment mechanisms
• be capable of handling installation, production environment setup, and architecture configuration tasks
• be able to assess code quality, perform debugging, and implement monitoring
• be proficient in implementing advanced production tasks such as model training, graph construction, and logging

This instructor-led, live training in Argentina (online or onsite) is designed for advanced professionals eager to explore cutting-edge XAI techniques for deep learning models, focusing on the creation of interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.